Abstract: A system for screening persons' temperatures comprises an imaging device configured to generate imagery data, including infrared image data, of a first scene and a second scene. The system receives imagery data from the first scene, identifies persons in the scene using the imagery data, and determines each person's temperature. The system further compares the persons' temperatures to a threshold and indicates a person should be directed to the second scene if the person's temperature is above a threshold. Subsequently, the system determines and compares the person's temperature in the second scene and provides an indication if the person's temperature exceeds a second threshold. Further, a confidence factor can be associated with determinations of persons' temperatures.

Abstract: Provided are a method for enhancing a video resolution enhancement, a computer readable storage medium, and an electronic device. The method includes: obtaining multiple image frames as input data, and obtaining initial data by performing feature extraction on the input data using a first three-dimensional convolutional layer; obtaining first feature data by performing down-sampling on the initial data at a preset multiple; obtaining first reference data by performing a convolution operation on the first feature data using a second three-dimensional convolutional layer to merge the first feature data as one frame; and obtaining first output data by performing up-sampling on the first reference data at the preset multiple.

Abstract: An optical coherence tomography (OCT) unit provides a mechanism/method for automatic artery and vein identification. The method may be embodied by specialized algorithm or by a machine learning model, such as a support vector machine or neural network. The present method may identify a vascular configuration that forms a vortex structure in the deep capillary plexus (DCP) of an OCT-based (e.g., OCTA) image of a retina. The identified vortex may then be designated a vein, and other vascular structures from other plexuses, such as from the superficial vascular plexus (SVP), that connect to the identified vortex structure are likewise designated veins. Additional criteria, some based on the vein designations, may be used to identify arteries.

Abstract: State information can be determined for a subject that is robust to different inputs or conditions. For drowsiness, facial landmarks can be determined from captured image data and used to determine a set of blink parameters. These parameters can be used, such as with a temporal network, to estimate a state (e.g., drowsiness) of the subject. To improve robustness, an eye state determination network can determine eye state from the image data, without reliance on intermediate landmarks, that can be used, such as with another temporal network, to estimate the state of the subject. A weighted combination of these values can be used to determine an overall state of the subject. To improve accuracy, individual behavior patterns and context information can be utilized to account for variations in the data due to subject variation or current context rather than changes in state.

Abstract: A method, computer program, and computer system is provided for predicting data from an image. An image is divided into one or more patch images. Spatial features corresponding to the one or more patch images are compressed. Output data corresponding to the compressed spatial features is predicted. The output data is predicted based on minimizing one or more loss functions corresponding to the compressed spatial features.

Abstract: An object recognition method using queue-based model selection and optical flow in an autonomous driving environment includes preprocessing data through a dense flow in a matrix form by calculating an optical flow of images captured consecutively in time by a sensor for an autonomous vehicle, generating a confidence mask by generating a vectorized confidence threshold representing a probability that there is a moving object for each cell of the preprocessed matrix, determining whether there is a moving object on the images by mapping the images captured consecutively in time to the confidence mask, and selecting an object recognition model using a tradeoff constant between object recognition accuracy and queue stability in each time unit. Accordingly, it is possible to improve the performance of object recognition in an autonomous driving environment by applying the optical flow to the confidence threshold of the object recognition system.

Abstract: An automatic modeling method for object detection model, the method includes: S1, searching for an NAS network model according to a predetermined first neural network; S2, training a first object detection model by using a training set, evaluating the current first object detection model by using a validation set after a number of training times reaches a first preset number of times, and outputting an evaluation result, wherein the first object detection model is a model obtained by detection part fusion of the NAS network model and a second object detection model; S3, calculating a reward corresponding to the current first object detection model; S4, adjusting parameters used for searching for the NAS network model according to the reward: repeating the steps S1-S4 for a second preset number of times, and determining an optimal first object detection model according to the evaluation result. Further disclosed in the present invention is an automatic modeling device.

Abstract: The present invention relates to a calculation device (10) for comparing dark-field X-ray images. The calculation device in (10) is configured for receiving a first dark-field X-ray image (11) describing first dark-field X-ray signals of a patient at an expiration state and for receiving a second dark-field X-ray image (12) describing second dark-field X-ray signals of the patient at an inspiration state. The calculation device is further (10) configured for normalizing the first dark-field X-ray signals of the first dark-field X-ray tin image (11) with a lung thickness value describing the lung thickness at the expiration state and for normalizing the second dark-field X-ray signals of the second dark-field X-ray image (12) with a lung thickness value describing the lung thickness at the inspiration state.

Abstract: One embodiment of the present invention sets forth a technique for extracting data from a document. The technique includes determining a first set of features associated with the document, wherein the first set of features comprises a set of region proposals that bound one or more portions of text within the document. The technique also includes applying a first machine learning model to the first set of features to generate a set of predictions associated with one or more key-value pairs included in the document. The technique further includes extracting the one or more key-value pairs from the document based on the set of predictions.

Abstract: File system manipulation using machine learning is described. Access to a machine learning system is obtained. A connection between a file system and an application is structured. The connection is managed through an application programming interface (API). The connection provides two-way data transfer through the API between the application and the file system. The connection provides distribution of one or more data files through the API. The connection provides enablement of processing of the one or more data files. The processing uses classifiers running on the machine learning system. Data files are retrieved from the file system connected through the interface. The file system is network-connected to the application through the interface. The data files comprise image data of one or more people. Cognitive state analysis is performed by the machine learning system. The application programming interface is generated by a software development kit (SDK).

Abstract: A non-transitory computer readable medium storing computer executable instructions which, when executed by processing circuitry, cause the processing circuitry to acquire, from a first sensor, data related to a face of a performer; provide first data, that is generated based on the data, to a classifier; receive, from the classifier, specific facial expression data indicating one specific facial expression among a plurality of predetermined specific facial expressions on the basis of the first data; and select, for display output, a specific facial expression corresponding to the specific facial expression data received from the classifier.

Abstract: Hierarchical systems and methods for automatic container type recognition from images are disclosed herein. An example embodiment includes a system for image analysis, comprising: a container recognition component; a character recognition component; and a 3D point cloud component; wherein the container recognition component is configured to receive an image and produce one of three outputs based on analysis of the image such that the output corresponds to either a container is identified, further analysis is performed by the character recognition component, or further analysis is performed by the 3D point cloud component.

Abstract: A video enhancement method and apparatus, an electronic device, and a storage medium are described. The method comprises: extracting features from M frames of images, so as to obtain at least one first-scale image feature (S310); for each first-scale image feature, performing N-level down-sampling processing on the first-scale image feature, so as to obtain a second-scale image feature (S320); performing N-level up-sampling processing on the second-scale image feature, so as to obtain a third-scale image feature (S330), wherein the input of ith-level up-sampling processing is an image feature obtained after performing superimposition processing on the output of (N+1?i)th-level down-sampling processing and the output of (i?1)th-level up-sampling processing, and the multiple of jth-level up-sampling is the same as the multiple of (N+1?j)th-level down-sampling; and performing superimposition processing on the third-scale image feature and the first-scale image feature.

Abstract: The present disclosure relates to security control methods and systems. The security control system may include at least one storage device storing a set of instructions; and one or more processors in communication with the at least one storage device, wherein when executing the set of instructions, the one or more processors are configured to direct the system to: obtain first data from a first device; obtain second data from a second device; associate and process the first data and/or the second data; and send the processed first data and second data to a server and/or a user terminal. The present disclosure can achieve linkage control of a plurality of access controls to meet the needs of users on indoor security.

Abstract: A compact generative neural network can be distilled from a teacher generative neural network using a training network. The compact network can be trained on the input data and output data of the teacher network. The training network train the student network using a discrimination layer and one or more types of losses, such as perception loss and adversarial loss.

Abstract: An image processing method is applied to an image display device, the image display device includes a lens having distortion coefficients and a display screen. The image processing method includes: dividing a display region of the display screen into image regions according to the distortion coefficients of the lens, an outer boundary line of each image region enclosing a polygon, a geometric center of the polygon enclosed by the outer boundary line of the image region coinciding with a geometric center of the lens, and distortion coefficients of positions of the lens on which vertexes of the polygon enclosed by the outer boundary line of the image region are mapped being the same; and performing anti-distortion processing on coordinates of vertexes of the image region according to a distortion coefficient corresponding to the vertexes of the image region to obtain texture coordinates of the vertexes of the image region.

Abstract: Computing platforms and methods are disclosed for verification of cleaning services to be performed in an area to be cleaned. Exemplary implementations may: identify, from among a plurality of cleaning regions relating to the area to be cleaned, a group of designated cleaning regions; randomly generate, with respect to the plurality of cleaning regions, a set of inspection regions; provide a user interface to prompt a user to capture an image of each inspection region; directly capture the images of each inspection region via the user interface; compare the captured images of each inspection region with corresponding reference images to determine whether each of the inspection regions has been properly cleaned; and output an inspection result based on the comparison of the captured images with the corresponding reference images. Implementations provide a method for virtually supervising otherwise unsupervised workers, which increases accountability and generates unprecedented visibility.

Abstract: A dishwasher comprises an image sensor, first and second drawers, first and second actuators configured to respectively extend or retract the first and second drawers, and a computer system. The computer system is configured to receive images from the image sensor, determine whether an object is within a threshold distance, and in response to determining that an object is moved to within the threshold distance, use a washwear recognition system to determine whether the object comprises an washwear item. A determined washwear type and a washwear dimension are used to identify an unoccupied location in of a size and configured suitable to receive the washwear item. In response to identifying an unoccupied location of a size and configured suitable to receive the washwear item, an actuator among the first actuator and the second actuator is caused to extend a corresponding drawer to thereby receive the item of dishware.

Abstract: High resolution object detection systems and methods provide accurate, real-time, one-stage processing, and include a backbone network configured to receive an input image and generate multi-scale feature representations, a feature fusion block configured to fuse the multi-sale feature representations, a plurality of representation transfer modules configured to isolate and decouple sub-task networks and the multi-scale feature representations, and a cascade refinement module configured to process each representation transfer module output to refine predictions. The backbone network generates a plurality of image features corresponding to each of a plurality of image scales and includes a plurality of convolutional layers and a stem block after the first convolutional layer, wherein the stem block improves feature extraction performance. The feature fusion block generates feature outputs for each of a plurality of image scales.

Abstract: Provided is a process for generating specifications for lenses of eyewear based on locations of extents of the eyewear determined through a pupil location determination process. Some embodiments capture an image and determine, using computer vision image recognition functionality, the pupil locations of a human's eyes based on the captured image depicting the human wearing eyewear.